Video frame interpolation. Video frame interpolation aims to estimate intermediate frames between two consecutive frames. They can be classified into three categories: kernel-based methods [1]}, [2]}, [3]}, [4]}, [5]}, phase-based methods [6]}, [7]}, and flow-based methods [8]}, [9]}, [10]}, [11]}, [12]}, [13]}, [14]}, [15]}, [16]}, [17]}, [18]}, [19]}, [20]}, [21]}, [22]}, [23]}, [24]}, [25]}, [26]}, [27]}, [28]}. Kernel-based methods [1]}, [2]}, [3]}, [5]} estimate a kernel for each pixel in the frame indicating the weights for its neighboring pixels to synthesize the new pixel. They generate the synthesized frame in a local convolutional manner. Phase-based methods [6]}, [7]} generate intermediate frames by per-pixel phase modification. Currently, most of methods are flow based [8]}, [9]}, [15]}, [16]}, [17]}, [18]}, [19]}, [20]}, [21]}, [22]}, [24]}, [25]}, [26]}, [27]}. Typically, their networks contain two modules: optical flow module and frame synthesized module. The optical flow module is used to estimate the optical flow between input frames. By warping the input frame based on the estimated optical flow, the frame synthesized module synthesizes the intermediate frames. Most of the video frame interpolation methods use PSNR and SSIM as their evaluation metrics. Some methods [16]}, [21]}, [51]} also explore Interpolation Error (IE) [52]}, Normalized Interpolation Error (NIE) [52]}, and Learned Perceptual Image Patch Similarity (LPIPS) [54]} to measure their results. However, these metrics are insufficient to evaluate the perceptual similarity. Traditional quality metrics, such as PSNR, only use low-level features and LPIPS lacks temporal information. In this paper, we propose a learned video quality metric dedicated to video frame interpolation.
